scholarly journals Sensor-Based Degradation Prediction and Prognostics for Remaining Useful Life Estimation: Validation on Experimental Data of Electric Motors

2020 ◽  
Vol 6 (3) ◽  
Author(s):  
Federico Barbieri ◽  
J. Wesley Hines ◽  
Michael Sharp ◽  
Mauro Venturini
Keyword(s):  
Failure Modes ◽  
Model Performance ◽  
Optimization Methods ◽  
Ordinary Least Squares ◽  
Remaining Useful Life ◽  
Path Model ◽  
Health State ◽  
Prognostic Parameter ◽  
Accelerated Degradation ◽  
Useful Life

Prognostics is an emerging science of predicting the health condition of a system and/or its components, based upon current and previous system status, with the ultimate goal of accurate prediction of the Remaining Useful Life (RUL). Based on this assumption, components/systems can be monitored to track the health state during operation. Acquired data are generally processed to extract relevant features related to the degradation condition of the component/system. Often, it is beneficial to combine several of these degradation parameters through an optimization process to develop a single parameter, called prognostic parameter, which can be trended to estimate the RUL. The approach adopted in this paper consists of a prognostic procedure which involves prognostic parameter generation and General Path Model (GPM) prediction. The Genetic Algorithm (GA) and Ordinary Least Squares (OLS) optimization methods will be used to develop suitable prognostic parameters from the selected features. Both time and frequency domain analysis will be investigated. Steady-state data obtained from electric motor accelerated degradation testing is used for method validation. Ten three-phase 5HP induction were run through temperature and humidity accelerated degradation cycles on a weekly basis. Of those, five presented similar degradation pathways due to bearing failure modes. The results show that the OLS method, on average, generated the best prognostic parameter performance using a combination of time domain features. However, the best single model performance was obtained using the GA methodology. In this case, the estimated RUL nearly coincided with the true RUL with an absolute percent error averaging under 5% near the end of life.

Failure Modes Mechanisms Effects Analysis for Refrigeration Device

2013 ◽  
Vol 288 ◽  
pp. 69-74 ◽  
Author(s):  
Ren Xiao Xu ◽  
Yang Liu
Keyword(s):  
Life Cycle ◽  
Failure Modes ◽  
Life Cycle Management ◽  
State Assessment ◽  
Remaining Useful Life ◽  
Automatic Identification ◽  
Health State ◽  
Useful Life ◽  
Using Data ◽  
Fusion Approach

FMMEA (failure mode, mechanisms, and effects analysis) is an effective tool for the life-cycle management of products and devices. We conducted an FMMEA for a refrigeration device at the request of a corporation. This paper demonstrates the process of our analysis of the compressor by employing Ganesan’s methodology. The results are listed in a table, including the physics of failures, risk priorities and parameters for monitoring. This paper also provides health-state assessment approaches based on FMMEA results and values of relevant parameters using fusion approach. Such assessment can be used for remaining useful life (RUL) estimation. Additionally, the paper illustrates our approach of computer-program-based automatic identification of failure using data of parameters retrieved from sensors.

scholarly journals Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Sergio Cofre-Martel ◽  
Enrique Lopez Droguett ◽  
Mohammad Modarres
Keyword(s):  
Deep Learning ◽  
Latent Variables ◽  
Latent Variable ◽  
Failure Modes ◽  
Remaining Useful Life ◽  
Space Representation ◽  
Health State ◽  
Partial Differential ◽  
Unseen Data ◽  
Useful Life

Remaining useful life (RUL) estimation is one of the main objectives of prognostics and health management (PHM) frameworks. For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. Although the state-of-art results have been achieved in benchmark problems, most DL-PHM algorithms are treated as black-box functions, giving little-to-no control over data interpretation. This becomes an issue when the models unknowingly break the governing laws of physics when no constraints are imposed. The latest research efforts have focused on applying complex DL models to achieve low prediction errors rather than studying how they interpret the data’s behavior and the system itself. This paper proposes an open-box approach using a deep neural network framework to explore the physics of a complex system’s degradation through partial differential equations (PDEs). This proposed framework is an attempt to bridge the gap between statistic-based PHM and physics-based PHM. The framework has three stages, and it aims to discover the health state of the system through a latent variable while still providing a RUL estimation. Results show that the latent variable can capture the failure modes of the system. A latent space representation can also be used as a health state estimator through a random forest classifier with up to a 90% performance on new unseen data.

scholarly journals Particle Filtering Based Remaining Useful Life Prediction for Electromagnetic Coil Insulation

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 473
Author(s):  
Haifeng Guo ◽  
Aidong Xu ◽  
Kai Wang ◽  
Yue Sun ◽  
Xiaojia Han ◽  
...  
Keyword(s):  
Particle Filtering ◽  
Remaining Useful Life ◽  
Electrical Parameters ◽  
Degradation Model ◽  
Accelerated Degradation ◽  
Insulation Failure ◽  
Electromagnetic Coils ◽  
Degradation Monitoring ◽  
Insulation Degradation ◽  
Useful Life

Electromagnetic coils are one of the key components of many systems. Their insulation failure can have severe effects on the systems in which coils are used. This paper focuses on insulation degradation monitoring and remaining useful life (RUL) prediction of electromagnetic coils. First, insulation degradation characteristics are extracted from coil high-frequency electrical parameters. Second, health indicator is defined based on insulation degradation characteristics to indicate the health degree of coil insulation. Finally, an insulation degradation model is constructed, and coil insulation RUL prediction is performed by particle filtering. Thermal accelerated degradation experiments are performed to validate the RUL prediction performance. The proposed method presents opportunities for predictive maintenance of systems that incorporate coils.

scholarly journals Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression

2021 ◽  
Vol 11 (11) ◽  
pp. 4773
Author(s):  
Qiaoping Tian ◽  
Honglei Wang
Keyword(s):  
Quantile Regression ◽  
Probability Density ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Health State ◽  
Rolling Bearings ◽  
Data Set ◽  
Time Frequency ◽  
Useful Life ◽  
Degradation Indicator

High precision and multi information prediction results of bearing remaining useful life (RUL) can effectively describe the uncertainty of bearing health state and operation state. Aiming at the problem of feature efficient extraction and RUL prediction during rolling bearings operation degradation process, through data reduction and key features mining analysis, a new feature vector based on time-frequency domain joint feature is found to describe the bearings degradation process more comprehensively. In order to keep the effective information without increasing the scale of neural network, a joint feature compression calculation method based on redefined degradation indicator (DI) was proposed to determine the input data set. By combining the temporal convolution network with the quantile regression (TCNQR) algorithm, the probability density forecasting at any time is achieved based on kernel density estimation (KDE) for the conditional distribution of predicted values. The experimental results show that the proposed method can obtain the point prediction results with smaller errors. Compared with the existing quantile regression of long short-term memory network(LSTMQR), the proposed method can construct more accurate prediction interval and probability density curve, which can effectively quantify the uncertainty of bearing running state.

scholarly journals Precision degradation prediction of inertial test turntable based on Hidden Markov Model and optimized particle filtering

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402097249
Author(s):  
Liming Li ◽  
Xunyi Zhou ◽  
Xingqi Zhang ◽  
Zhenghu Zhong
Keyword(s):  
Markov Model ◽  
Prediction Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Vibration Signal ◽  
Remaining Useful Life ◽  
Health State ◽  
Starting Point ◽  
Useful Life ◽  
Position Precision

In order to solve the problem that there is no effective evaluation method for the precision degradation state of inertial test turntable, a prediction model for the position precision degradation trend of test turntable was proposed based on the Hidden Markov Model (HMM) algorithm and Particle Filter (PF) algorithm. The initial parameter of the PF algorithm was optimized by the Particle Swarm Optimization (PSO) algorithm. The vibration signal was selected as the research data, which could be obtained from an velocity test of turntable precision degradation. Firstly, the original vibration signal was denoised by Ensemble Empirical Mode Decomposition and Principal Component Analysis (EEMD-PCA) algorithm, and the signal with fault characteristic was extracted for signal reconstruction; Secondly, a HMM model could be trained by using the statistical characteristic values as observation matrix, and the diagnosis of early position precision degradation and the health state indexes could be obtained. Finally, a prediction model of the test turntable precision degradation could be established by using PF algorithm, and the Remaining Useful Life (RUL) of the test turntable precision could be calculated. When the 50th group data were taken as the prediction starting point, the predicted remaining useful life was 21 years, and the actual measured result was 17 years, which are close to each other. Comparing the model calculation results and the test measurement results, it is shown that the model could effectively and accurately predict the change trend and remaining useful life of the test turntable precision.

scholarly journals Heat Exchanger Fouling and Estimation of Remaining Useful Life

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Tutpol Ardsomang ◽  
J. Wesley Hines ◽  
Belle R. Upadhyaya
Keyword(s):  
Heat Exchanger ◽  
Process Management ◽  
Remaining Useful Life ◽  
Path Model ◽  
Empirical Modeling ◽  
Data Driven ◽  
Transfer Resistance ◽  
Failure Data ◽  
Real Process ◽  
Useful Life

One of the challenges in data-driven prognostics is the availability of degradation data for application to prognostic methods. In real process management settings, failure data are not often available due to the high costs of unplanned breakdowns. This research presents a data-driven (empirical) modeling approach for characterizing the degradation of a heat exchanger (HX) and to estimate the Remaining Useful Life (RUL) of its design operation. The Autoassociative Kernel Regression (AAKR) modeling was applied to predict the effect of fouling on the heat transfer resistance. The result indicates that AAKR model is an effective method to capture the HX fouling in the dynamic process. The AAKR residuals were fused to develop a prognostic parameter which was used to develop a General Path Model (GPM) with Bayesian updating. The results demonstrate the successful application of this approach for the heat exchanger RUL prediction.

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